Method for providing an image base on a reconstructed image group and an apparatus using the same

ABSTRACT

An image providing method performed by a computing apparatus includes acquiring a first image group including at least a portion of a series of images generated for continuous volumes with a first slice thickness belonging to a subject, providing, as a current viewing image, one image of the first image group or one image of a second image group including images generated for continuous volumes with a second slice thickness belonging to the subject, and in response to a first specific input of an input device, repeatedly updating an image provided as the current viewing image with an individual image provided for a subsequent viewing based on a directivity given for the first specific input and, in response to a second specific input of the input device, switching the current viewing image between an image of the first image group and an image of the second image group.

This application claims priority from and the benefit of Korean PatentApplication No. 10-2019-0098920 filed on Aug. 13, 2019, which is herebyincorporated by reference in its entirety.

BACKGROUND 1. Field

The present disclosure of the following description relates to an imageproviding method and an apparatus for performing the image providingmethod.

2. Related Art

Currently, computed tomography (CT) technology is widely used as animaging test to analyze lesions and use the same for diagnosis.Individual images constituting a CT image are acquired by projecting avolume with a predetermined slice thickness onto a plane. Here, athickness of the individual images is referred to as a slice thicknessof the CT image for convenience. For example, a 5 mm slice thick imagerefers to an image acquired by combining information of a 5 mm slicethick space into a single image and thus, the image is blurry, that is,has a low quality.

The thickness of the CT image is differently reconstructed based on thepurpose and environment of CT reading. As the thickness becomes thinner,the quality of the image and the accuracy of reading may improve. On thecontrary, a number of CT images increases and a relatively long periodof time is used for reading accordingly. Also, a relatively largestorage space is required to store an image with a thin slice thickness.

In general, in the case of a chest CT image, a 5 mm slice thick image isstored in a database to save a storage space and achieve efficiency ofsubsequent reading. Here, a small nodule of less than 5 mm based on the5 mm slice thick image is highly likely to be damaged based on animaging characteristic. Therefore, to conduct a precise inspection,there is a need for an effective interface capable of alternatelyverifying a CT image with a 5 mm slice thickness and a relatively thinCT image.

Reference material may include Non-Patent Document 1: Chao Dong et al.(Image Super-Resolution Using Deep convolutional Networks, arXivpreprint arXiv:1501.00092v3, 2015)

SUMMARY

At least one example embodiment provides an interface capable ofeffectively switching between an image corresponding to a relativelythick slice thickness and an image corresponding to a relatively thinslice thickness and allowing the switched image to be viewed.

At least one example embodiment provides a method that may generate animage of an image group corresponding to a relatively thin slicethickness from an image of an image group corresponding to a relativelythick slice thickness and may readily switch between an image of a thickslice thickness and an image of a thin slice thickness, to assist adoctor to derive a more accurate diagnostic result and to improve ananalysis accuracy by a reading assistance system.

Characteristic constitutions of the disclosure to accomplish theaforementioned objectives and to achieve characteristic effects of thedisclosure are as follows:

According to an aspect of at least one example embodiment, there isprovided an image providing method performed by a computing apparatus,the image providing method including, by the computing apparatus, (a)acquiring a first image group including at least a portion of a seriesof images generated for continuous volumes with a first slice thicknessbelonging to a subject or supporting another apparatus interacting withthe computing apparatus to acquire the first image group: (b) providingor supporting the other apparatus to provide, as a current viewingimage, a single image of the first image group or a single image of asecond image group including a series of images generated for continuousvolumes with a second slice thickness belonging to the subject; (c)performing a process (c1) of, in response to a first specific input ofan input device, repeatedly updating or supporting the other apparatusto update an image provided as the current viewing image with anindividual image determined to be provided for a subsequent viewingbased on a directivity given for the first specific input and a process(c2) of, in response to a second specific input of the input device,switching or supporting the other apparatus to switch the currentviewing image to one of between an image belonging to the first imagegroup and an image belonging to the second image group.

The second slice thickness may be less than the first slice thickness.

The first image group may be generated by projecting, onto a plane, atleast a portion of the series of images generated for the continuousvolumes with the first slice thickness belonging to the subject, and thesecond image group may be generated from the first image group based ona super-resolution (SR) scheme.

According to another aspect of at least one example embodiment, there isprovided a non-transitory computer-readable record medium storinginstructions that, when executed by a processor, cause the processor toperform the image providing method.

According to still another aspect of at least one example embodiment,there is provided a computing apparatus for providing an image generatedbased on subject information of different slice thicknesses, thecomputing apparatus including a communicator configured to receive auser input; and a processor configured to perform a process of acquiringa first image group including at least a portion of a series of imagesgenerated for continuous volumes with a first slice thickness belongingto a subject or supporting another apparatus interacting with thecomputing apparatus to acquire the first image group, a process ofproviding or supporting the other apparatus to provide, as a currentviewing image, a single image of the first image group or a single imageof a second image group including a series of images generated forcontinuous volumes with a second slice thickness belonging to thesubject, a process of, in response to a first specific input of an inputdevice, repeatedly updating or supporting the other apparatus to updatean image provided as the current viewing image with an individual imagedetermined to be provided for a subsequent viewing based on adirectivity given for the first specific input and a process of, inresponse to a second specific input of the input device, switching orsupporting the other apparatus to switch the current viewing imagebetween an image belonging to the first image group and an imagebelonging to the second image group.

According to some example embodiments, through an interface ofincreasing a reading speed using an image corresponding to a relativelythick slice thickness with respect to an area for which an imagecorresponding to a relatively thin slice thickness is not required andperforming reading using a reconstructed image with a relatively thinslice thickness with respect to an area for which a further precisedetermination is required, based on a judgement of a reader, it ispossible to improve the accuracy of reading and to save a reading time.

For example, according to some example embodiments, it is possible toinnovate a workflow in a medical field by saving a time used for themedical staff to perform a diagnosis and by improving a speed andquality of reading.

According to some example embodiments, since it is possible to usemedical images used in hospitals in the related art, such as, forexample, three-dimensionally acquired ultrasound images, magneticresonance imaging (MRI) images, etc., a method proposed herein is notdependent on a particular type of an image or platform.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE FIGURES

Example embodiments will be described in more in detail with referenceto the following figures that are simply a portion of the exampleembodiments and those having ordinary skill in the art (hereinafter,those skilled in the art) to which this disclosure pertains may readilyacquire other figures based on the figures without an inventive workbeing made:

FIG. 1 is a diagram illustrating an example of a configuration of acomputing apparatus configured to perform an image providing methodaccording to an example embodiment;

FIG. 2 is a diagram illustrating an example of hardware or softwarecomponents of a computing apparatus configured to perform an imageproviding method according to an example embodiment;

FIG. 3 is a flowchart illustrating an example of an image providingmethod according to an example embodiment;

FIG. 4 illustrates an example of describing a method of generating andstoring an image of a second image group according to an exampleembodiment; and

FIG. 5 illustrates an example of applying an image providing method by acomputing apparatus according to an example embodiment.

DETAILED DESCRIPTION

The following detailed description of this disclosure is described withreference to the accompanying drawings in which specific exampleembodiments of the disclosure are illustrated as examples, to fullydescribe purposes, technical solutions, and advantages of thedisclosure. The example embodiments are described in detail enough forthose skilled in the art to carry out the disclosure.

The terms “image” and “image data” used throughout the detaileddescription and the claims herein refer to multi-dimensional data thatincludes discrete image factors (e.g., a pixel in a two-dimensional (2D)image and a voxel in a three-dimensional (3D) image). For example, theterm “image” may refer to a medical image of a subject collected bycone-beam computed tomography (CBCT), magnetic resonance imaging (MRI),an ultrasound system, or known other medical imaging systems in therelated art. Also, the image may be provided in a non-medical context,for example, a remote sensing system, an electron microscopy, and thelike.

The term “image” used throughout the detailed description and the claimsmay refer to an image visible with an eye (e.g., displayed on a videoscreen) or a digital representation of an image (e.g., a filecorresponding to a pixel output of a CT, an MRI detector, and the like).

For clarity of description, although CBCT image data is illustrated inthe drawings as image modality, image forms used in various exampleembodiments include X-ray images, MRI, CT, positron emission tomography(PET), PET-CT, single photo emission computed tomography (SPECT),SPECT-CT, MR-PET, 3D ultra sound images, etc. However, it will beapparent to those skilled in the art that any 3D image and a slide imagederived therefrom may be available, without being limited thereto.

The term “Digital Imaging and Communications in Medicine (DICOM)”standard used throughout the detailed description and the claims is ageneric term for a plurality of standards used for digital imagerepresentation and communication in medical devices. The DICOM standardis published by the American College of Radiology (ACR) and the NationalElectrical Manufacturers Association (NEMA).

Also, the term “Picture Archiving and Communication System (PACS)” usedthroughout the detailed description and the claims is a term for systemsthat perform storage, processing, and transmission according to theDICOM standard. A medical image acquired using digital medical imagingequipment such as X-ray, CT, and MRI may be stored in a DICOM format andmay be transmitted to a terminal inside or outside a hospital over anetwork. Here, a reading result and a medical record may be added to themedical image.

Further, the term “training” or “learning” used throughout the detaileddescription and the claims refers to performing a machine learningthrough computing according to a procedure and it will be apparent tothose skilled in the art that the term is not intended to refer to amental action such as an educational activity of a human.

Also, the terms “comprises/includes” used throughout the detaileddescription and the claims and modifications thereof are not intended toexclude other technical features, additions, components, or operations.Also, “single” or “one” is used to indicate at least one and “another”is limited to at least second or more.

Those skilled in the art may clearly understand a portion of otherpurposes, advantages, and features of the disclosure from thisspecification and another portion thereof from implementations of thedisclosure. The following examples and drawings are provided as examplesonly and not to limit the disclosure. Therefore, the detaileddescription disclosed herein should not be interpreted as a limitingmeaning with respect to a specific structure or function and should beinterpreted as representative basic data that provides guidelines suchthat those skilled in the art may variously implement the disclosure assubstantially suitable detailed structures.

Further, the disclosure may include any possible combinations of exampleembodiments described herein. It should be understood that, althoughvarious example embodiments differ from each other, they do not need tobe exclusive. For example, a specific shape, structure, and featuredescribed herein may be implemented as another example embodimentwithout departing from the spirit and scope of the disclosure. Also, itshould be understood that a location or an arrangement of an individualcomponent of each disclosed example embodiment may be modified withoutdeparting from the spirit and scope of the disclosure. Accordingly, thefollowing detailed description is not to be construed as being limitingand the scope of the disclosure, if properly described, is limited bythe claims, their equivalents, and all variations within the scope ofthe claims. In the drawings, like reference numerals refer to likeelements throughout.

Unless the context clearly indicates otherwise, the singular forms “a,”“an,” and “the,” are intended to include the plural forms as well. Also,when description related to a known configuration or function is deemedto render the present disclosure ambiguous, the correspondingdescription is omitted.

Hereinafter, example embodiments of the disclosure are described indetail with reference to the accompanying drawings such that thoseskilled in the art may easily perform the example embodiments.

FIG. 1 is a diagram illustrating an example of a configuration of acomputing apparatus configured to perform an image providing methodaccording to an example embodiment.

Referring to FIG. 1, a computing apparatus 100 according to an exampleembodiment includes a communicator 110 and a processor 120, and maydirectly or indirectly communicate with an external computing apparatus(not shown) through the communicator 110.

In detail, the computing apparatus 100 may achieve a desired systemperformance using a combination of typical computer hardware (e.g., anapparatus including a computer processor, a memory, a storage, an inputdevice and an output device, components of other existing computingapparatuses, etc.; an electronic communication apparatus such as arouter, a switch, etc.; an electronic information storage system such asa network-attached storage (NAS) and a storage area network (SAN)) andcomputer software (i.e., instructions that enable a computing apparatusto function in a specific manner).

The communicator 110 of the computing apparatus 100 may transmit andreceive a request and a response with another interacting computingapparatus. As an example, the request and the response may beimplemented using the same transmission control protocol (TCP) session.However, it is provided as an example only. For example, the request andthe response may be transmitted and received as, for example, a userdatagram protocol (UDP) datagram. In addition, in a broad sense, thecommunicator 110 may include a keyboard, a mouse, and other externalinput devices to receive a command or an instruction, etc., and aprinter, a display, and other external output devices.

Also, the processor 120 of the computing apparatus 100 may include ahardware configuration, such as a micro processing unit (MPU), a centralprocessing unit (CPU), a graphics processing unit (GPU), a tensorprocessing unit (TPU), a cache memory, a data bus, and the like. Also,the processor 120 may further include a software configuration of anapplication that performs a specific objective, an operating system(OS), and the like.

FIG. 2 is a diagram illustrating an example of hardware or softwarecomponents of a computing apparatus configured to perform an imageproviding method according to an example embodiment.

Those skilled in the art may understand that individual modules of FIG.2 may be configured through, for example, the communicator 110 or theprocessor 120 included in the computing apparatus 100, or throughinteraction between the communicator 110 and the processor 120.

Describing a method and a configuration of an apparatus according to anexample embodiment with reference to FIG. 2, the computing apparatus 100may include an image acquisition module 210 as a component. The imageacquisition module 210 may acquire an image included in a first imagegroup that is prestored in a database or acquired from a dedicateddevice for image capturing. The image included in the first image groupmay refer to an image that is generated by projecting, onto a plane,continuous volumes with a first slice thickness belonging to a subject.The image of the first image group may be an axial image of the subject.Also, although it is described that images of the first image group andthe second image group are generated based on a chest CT image forclarity of description, it may be understood that they may apply to allof general 3D medical images. An image belonging to the first imagegroup may be an image that is generated based on volume information of arelatively thick first slice thickness to save a storage space and toincrease a reading speed.

The acquired image of the first image group may be forwarded to an imagegeneration module 220. The image generation module 220 may generate atleast one image of the second image group based on the forwarded image.The image of the second image group may refer to an image correspondingto a second slice thickness less than the first slice thickness.

The image generation module 220 may include an artificial neural networktrained based on a large number of images of the first image group andimages of the second image group corresponding thereto. The imagegeneration module 220 is configured to regenerate an image of the secondimage group that meets a feature extracted from an image of the firstimage group. For example, the image generation module 220 may use a fullconvolutional neural network that is a deep neural network configured togenerate an image of the second image group from an image of the firstimage group. Also, the image generation module 220 may be trained toreceive the first slice thickness and the second slice thickness as aparameter and to generate the received image of the first image group asthe image of the second image group corresponding to the second slicethickness. The image generation module 220 may be pretrained by using,as training data, a plurality of training image pairs each including afirst training image of the first slice thickness and a second trainingimage of the second slice thickness.

Meanwhile, it is known that a super-resolution (SR) scheme of convertinga low resolution image to a high resolution image, that is, increasing aresolution is available. Such SR scheme is described in, for example,Non-Patent Document 1: [Chao Dong et al. Image Super-Resoution UsingDeep Convolutional Networks, arXiv preprint arXiv:1501.00092v3, 2015]Since the SR scheme described in this document also extracts a featureof an input image and regenerates an output image suitable for thefeature, those skilled in the art may understand that an image of thesecond image group may be generated by applying the SR scheme.

An image storage and transmission module 230 may store the generatedimage of the second image group. The image storage and transmissionmodule 230 may store the image of the second image group in the databaseto match the image of the first image group that is used to generate theimage of the second image group. Also, the image storage andtransmission module 230 may sort the generated images of the secondimage group based on a mutual positional relationship between the imagesof the second image group and may store the images of the second imagegroup based on a sorting result. For example, the image storage module230 may sort the images of the second image group in order of physicallocations of the images of the second image group, that is, depth valuescorresponding to the images and may store the sorted images in thedatabase.

The image storage and transmission module 230 may provide the externalentity with the image of the first image group or the image of thesecond image group stored in the database. Here, in the case ofproviding the external entity, the image storage and transmission module230 may provide the external entity with the image of the first imagegroup or the image of the second image group through a predetermineddisplay device or through a communicator provided therein. The imagestorage and transmission module 230 may selectively provide the externalentity with the image of the first image group or the image of thesecond image group in response to a request from the reader.

Here, the external entity may include a user of the computing apparatus100, a manager, a medical expert in charge of the subject, and the like.In addition, it may be understood that any entity that needs the imageof the second image group produced from the image of the first imagegroup may be included as the external entity. For example, the externalentity may be an external artificial intelligence (AI) device thatincludes separate AI hardware module and/or software module using theimage of the second image group. Also, “external” in the external entityis not construed to exclude an example embodiment in which AI hardwaremodule and/or software module using at least one of the image of thefirst image group and the image of the second image group are integratedinto the computing apparatus 100 and is used to represent that a resultof hardware module and/or software module performing the method of thepresent disclosure, for example, the image of the second image group, isavailable as input data of another method. That is, the external entitymay be the computing apparatus 100 itself.

Meanwhile, the generated image of the second image group may be used fora doctor to easily perform reading and diagnosis.

Based on such images of the first image group and the images of thesecond image group that are matched and thereby stored, the imagestorage and transmission module 230 may provide a method that allows areader to effectively read an image. In the case of a conventionalmethod of individually generating, from a subject, all of an imagecorresponding to a relatively thick slice thickness and an imagecorresponding to a relatively thin slice thickness and storing thegenerated images and alternately verifying independent two imagesdepending on a necessity of the reader to improve the accuracy ofreading, the reader needs to alternately verify the independent twoimages in person, which may cause disconnection of reading. In contrast,herein, an image of the first image group and an image of the secondimage group are matched to each other and images of the second imagegroup are sorted based on a mutual positional relationship. Therefore,it is possible to provide a method capable of quickly and accuratelyperforming reading without disconnection during an image readingprocess.

Although FIG. 2 illustrates that the components are implemented in asingle computing apparatus for clarity of description, a plurality ofcomputing apparatus 100 configured to perform the method disclosedherein may be configured to interact with each other.

Hereinafter, an image providing method according to an exampleembodiment is further described with reference to FIGS. 3 to 5.

FIG. 3 is a flowchart illustrating an example of an image providingmethod according to an example embodiment.

Referring to FIG. 3, in operation S100, a computing apparatus mayacquire a first image group including at least a portion of a series ofimages generated for continuous volumes with a first slice thicknessbelonging to a subject or may support another apparatus interacting withthe computing apparatus to acquire the first image group. According toan example embodiment, the first image group may be generated byproject, onto a plane, at least a portion of the series of imagesgenerated for the continuous volumes of the first slice thicknessbelonging to the subject.

In operation S200, the computing apparatus may provide or support theother apparatus to provide, as a current viewing image, a single imageof the first image group or a single image of a second image groupincluding a series of images generated for continuous volumes with asecond slice thickness belonging to the subject. According to an exampleembodiment, the second slice thickness may be less than the first slicethickness. The second image group may be generated from the first imagegroup based on an SR scheme.

In operation S300, in response to a first specific input of an inputdevice, the computing apparatus may perform a process of repeatedlyupdating or supporting the other apparatus to update an image providedas the current viewing image with an individual image determined to beprovided for a subsequent viewing based on a directivity given for thefirst specific input. Here, the first specific input refers to an inputfor updating the current viewing image and may be a directional input,that is, an input having a directivity. For example, the first specificinput may be an input through a mouse scroll having a directivity or aninput using a navigation key of a keyboard. In response to an input ofthe mouse scroll in an upward direction, the computing apparatus mayupdate the current viewing image with an image corresponding to alocation directly above in an axial direction of the current viewingimage. On the contrary, in response to an input of the mouse screen in adownward direction, the computing apparatus may update the currentviewing image with an image corresponding to a location directly belowin the axial direction of the current viewing image. The first specificinput is not limited to the proposed example and may include any methodcapable of providing a directional input.

In operation S300, in response to a second specific input of the inputdevice, the computing apparatus may switch or support the otherapparatus to switch the current viewing image between an image belongingto the first image group and an image belonging to the second imagegroup. For example, if the current viewing image is an image of thefirst image group and the second specific input is performed, thecomputing apparatus may switch the current viewing image to an imageincluded in the second image group corresponding to the current viewingimage. As described above, since images of the second image group arematched to an image of the first image group, image switching may beimmediately performed, which may lead to preventing disconnection ofreading.

According to an example embodiment, the second specific input may beperformed based on a toggle key method. For example, if the secondspecific input is received based on a toggle key in a situation in whichan image of the first image group is provided as the current viewingimage, the computing apparatus may provide, as the current viewingimage, an image belonging to the second image group corresponding to thecurrent viewing image provided at a point in time at which the secondspecific input is received. Subsequently, if the second specific inputis received again based on the toggle key, the computing apparatus mayprovide, as the current viewing image, an image of the first image groupcorresponding to the current viewing image provided at a point in timeat which the second specific input is received again.

According to another example embodiment, while a predetermined userinput corresponding to the second specific input is being maintained,the computing apparatus may provide, as the current viewing image, animage included in the second image group. In detail, if the secondspecific input is initiated and maintained for 3 seconds in a situationin which an image included in the first image group is provided as thecurrent viewing image, the computing apparatus may provide, as a currentimage, an image of the second image group corresponding to a currentviewing image provided at a point in time at which the second specificinput is received and may provide, as the current image, the image ofthe second image group for 3 seconds for which the second specific inputis maintained. If the first specific input is performed in a situationin which the second specific input is maintained, the computingapparatus may perform an operation of updating the current viewing imageusing the image of the second image group. That is, if the firstspecific input corresponding to the upward direction is received in asituation in which the second specific input is maintained, thecomputing apparatus may update the current viewing image with an imageof the second image group corresponding to a location directly above inthe axial direction of the current viewing image.

Hereinafter, an example of providing an image through the computingapparatus is further described with reference to FIG. 4.

FIG. 4 illustrates an example of describing a method of generating andstoring an image of a second image group according to an exampleembodiment.

Referring to FIG. 4, as described above, the computing apparatus maygenerate images 421, 422, 423, 424, and 425 corresponding to a secondimage group from a prestored image 410 of a first image group. Forexample, the image 410 may be an image included in the first image groupcorresponding to a 5 mm slice thickness. Each of the images 421, 422,423, 424, and 425 may be an image included in the second image groupcorresponding to a 1 mm slice thickness. The five images 421, 422, 423,424, and 425 may be generated based on the image 410 corresponding tothe 5 mm slice thickness. The computing apparatus may mutually match theimage 410 and the images 421, 422, 423, 424, and 425 and may store thesame in a database. For example, the images 421, 422, 423, 424, and 425generated based on the image 410 may be matched to the image 410 andthereby stored, and the images 421, 422, 423, 424, and 425 may be sortedand stored based on physical location information, for example, in orderof corresponding depth information. Here, the image 410 and the images421, 422, 423, 424, and 425 may be matched through matching betweenimage identification numbers. For example, if an identification numberof the image 410 is A1 and identification numbers of the images 421,422, 423, 424, and 425 are B1, B2, B3, B4, and B5, A1 and B1 to B5 maybe matched and stored in the database. In this manner, all of imagesincluded in the second image group may be matched to an image of thefirst image group corresponding thereto and thereby stored in thedatabase. Also, the images included in the second image group may besorted based on information about a positional relationship between theimages and thereby sorted in the database.

FIG. 5 illustrates an example of applying an image providing method by acomputing apparatus according to an example embodiment.

Referring to FIG. 5, each of images 510 and 520 may be an image providedfor a reader through an output device. A first specific input 531 refersto an input for the reader to update a current viewing image 512 and mayinclude an input that provides a directivity, such as a mouse scroll, anavigation button of a keyboard, and the like.

In response to receiving the first specific input 531 in a situation inwhich the image 512 of a first image group corresponding to a 5 mm slicethickness is provided as an initial image for the reader, the computingapparatus may update an image 521. In detail, the reader may view animage 513 corresponding to 5 mm above in a depth axis compared to thecurrent viewing image 512 in response to an input of a mouse scroll inan upward direction, or may read an image 511 corresponding to 5 mmbelow in the depth axis compared to the current viewing image 512 inresponse to an input of the mouse scroll in a downward direction.

For further precise reading, if an image 520 of a relatively thinnerslice thickness compared to the current viewing image 510 correspondingto the 5 mm slice thickness needs to be viewed, the reader may performimage switching through a second specific input 532. For example, inresponse to receiving the second specific input 532 from the readerwhile viewing the current viewing image 512, the computing apparatus mayprovide one of images 521, 522, 523, 524, and 525 that are matched tothe current viewing image 512 and thereby pre-stored. In FIG. 5, each ofthe images 521, 522, 523, 524, and 525 may be an image with a 1 mm slicethickness generated based on the image 512, and each of images 526 and527 may be an image of a 1 mm slice thickness generated based on theimage 513.

According to an example embodiment, the computing apparatus may providethe switched image 520 while the second specific input 532 is beingmaintained and may provide the image 510 again if the second specificinput 532 is terminated. In a situation in which the second specificinput 532 is maintained, the reader may perform updating among theimages 521, 522, 523, 524, 525, and 526 through the first specific input531. This image updating method may be identical to the method describedabove for the image 510.

If the second specific input 532 is terminated, image switching may beperformed to the image 510 corresponding to a current viewing image, forexample, the image 525, at a point in time at which the second specificinput 532 is terminated.

According to another example embodiment, the second specific input 532may be performed based on a toggle key method. For example, if thereader performs the second specific input 532, the reader maycontinuously view the switched image 520 until the second specific input532 is received again, without a need to maintain the correspondinginput.

One of ordinary skill in the art may easily understand that the methodsand/or processes and operations described herein may be implementedusing hardware components, software components, or a combination thereofbased on the example embodiments. For example, the hardware componentsmay include a general-purpose computer and/or exclusive computingapparatus or a specific computing apparatus or a special feature orcomponent of the specific computing apparatus. The processes may beimplemented using at least one microprocessor having an internal and/orexternal memory, a microcontroller, an embedded microcontroller, aprogrammable digital signal processor or other programmable devices. Inaddition, or, as an alternative, the processes may be implemented usingan application specific integrated circuit (ASIC), a programmable gatearray, a programmable array logic (PAL), or other devices configured toprocess electronic signals, or combinations thereof. Targets oftechnical solutions of the disclosure or portions contributing to thearts may be configured in a form of program instructions performed byvarious computer components and stored in non-transitorycomputer-readable recording media. The media may include, alone or incombination with the program instructions, data files, data structures,and the like. The program instructions recorded in the media may bespecially designed and configured for the example embodiments, or may beknown to those skilled in the art of computer software. Examples of themedia may include magnetic media such as hard disks, floppy disks, andmagnetic tapes; optical media such as CD-ROM discs, DVDs, and Blu-ray;magneto-optical media such as floptical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas ROM, RAM, flash memory, and the like. Examples of programinstructions may include a machine code, such as produced by a compilerand higher language code that may be executed by a computer using aninterpreter. Examples of program instructions include both machine code,such as produced by a compiler and files containing structuralprogramming languages such as C++ object-oriented programming languageand high or low programming languages (assembly languages, hardwaretechnical languages, database programming languages and techniques) torun not only on one of the aforementioned devices but also a processor,a processor architecture, or a heterogeneous combination of combinationsof different hardware and software components, or a machine capable ofexecuting program instructions. Accordingly, they may include a machinelanguage code, a byte code, and a high language code executable using aninterpreter and the like.

Therefore, according to an aspect of at least one example embodiment,the aforementioned methods and combinations thereof may be implementedby one or more computing apparatuses as an executable code that performsthe respective operations. According to another aspect, the methods maybe implemented by systems that perform the operations and may bedistributed over a plurality of devices in various manners or all of thefunctions may be integrated into a single exclusive, stand-alone device,or different hardware. According to another aspect, devices that performoperations associated with the aforementioned processes may include theaforementioned hardware and/or software. Such all of the sequences andcombinations associated with the processes are to be included in thescope of the present disclosure.

For example, the described hardware devices may be configured to act asone or more software modules in order to perform the operations of theabove-described example embodiments, or vice versa. The hardware devicesmay include a processor, such as, for example, an MPU, a CPU, a GPU, anda TPU, configured to be combined with a memory such as ROM/RAMconfigured to store program instructions and to execute the instructionsstored in the memory, and may include a communicator capable oftransmitting and receiving a signal with an external device. Inaddition, the hardware devices may include a keyboard, a mouse, and anexternal input device for receiving instructions created by developers.

While this disclosure is described with reference to specific matterssuch as components, some example embodiments, and drawings, they aremerely provided to help general understanding of the disclosure and thisdisclosure is not limited to the example embodiments. It will beapparent to those skilled in the art that various changes andmodifications in forms and details may be made from the exampleembodiments.

Therefore, the scope of this disclosure is not defined by the exampleembodiments, but by the claims and their equivalents, and all variationswithin the scope of the claims and their equivalents are to be construedas being included in the disclosure.

Such equally or equivalently modified example embodiments may include,for example, logically equivalent methods capable of achieving the sameresults as those acquired by implementing the method according to theexample embodiments. Accordingly, the present disclosure and the scopethereof are not limited to the aforementioned example embodiments andshould be understood as a widest meaning allowable by law.

What is claimed is:
 1. An image providing method performed by acomputing apparatus, the image providing method comprising: acquiring,by the computing apparatus, first images with a first slice thicknessbelonging to a subject; acquiring, by the computing apparatus, secondimages with a second slice thickness, thinner than the first slicethickness, belonging to the subject, wherein the second images include aplurality of second image sets, and each second image set includes aplurality of images generated from a corresponding first image based ona neural network and stored in a database so that a matchingrelationship is made between the first and second images, based on: (i)each second image set is matched to a respective one of the firstimages, with reference to a 1^(st) image of the each second image set,and (ii) a plurality of images in each second image set is arrangedbased on a mutual positional relationship, providing, by the computingapparatus, one of the first and second images; performing, by thecomputing apparatus, a first process of, in response to a first inputupdating an image provided as a current viewing image with an imagedetermined to be provided for a subsequent viewing based on adirectivity given for the first input and a second process of, inresponse to a second input, switching the current viewing image betweena first image and a corresponding second image based on the matchingrelationship between the first and second images.
 2. The image providingmethod of claim 1, wherein the second process is performed in responseto the second input based on a toggle key scheme.
 3. The image providingmethod of claim 1, wherein the second process is performed while apredetermined user input corresponding to the second input ismaintained.
 4. A non-transitory computer-readable record medium storinginstructions that, when executed by a processor of a computingapparatus, cause the processor to perform the image providing method ofclaim
 1. 5. The image providing method of claim 1, wherein each secondimage set is matched to the corresponding first image, based onidentification numbers of the first and second images.
 6. The imageproviding method of claim 1, wherein the neural network includes aneural network pre-trained by using, as training data, a plurality oftraining image pairs each including a first training image of the firstslice thickness and a second training image of the second slicethickness.
 7. A computing apparatus for providing an image, thecomputing apparatus comprising: a communicator configured to receive auser input; and a processor configured to perform a process of acquiringfirst images with a first slice thickness belonging to a subject, aprocess of acquiring second images with a second slice thickness,thinner than the first slice thickness, belonging to the subject,wherein the second images include a plurality of second image sets, andeach second image set includes a plurality of images generated from acorresponding first image based on a neural network and stored in adatabase so that a matching relationship is made between the first andsecond images, based on: (i) each second image set is matched to arespective one of the first images, with reference to a 1^(st) image ofthe each second image set, and (ii) a plurality of images in each secondimage set is arranged based on a mutual positional relationship, and aprocessor configured to perform a process of providing one of the firstand second images, a process of, in response to a first input, updatingan image provided as a current viewing image with an image determined tobe provided for a subsequent viewing based on a directivity given forthe first input, and a process of, in response to a second input,switching the current viewing image between a first image and acorresponding second image based on the matching relationship betweenthe first and second images.
 8. An image providing method performed by acomputing apparatus, the image providing method comprising: providing,by the computing apparatus, first images corresponding to a first slicethickness belonging to a subject, in order of a corresponding locationbased on a directivity of a first user input; and in response toreceiving a second user input, providing, by the computing apparatus,second images corresponding to a second slice thickness belonging to thesubject, in order of a corresponding location based on the directivity,starting from a second image corresponding to a location of a firstimage, which is provided at a point in time at which a second user inputis received, based on a matching relationship between the first andsecond images, wherein the second slice thickness is thinner than thefirst slice thickness, and wherein the second images include a pluralityof second image sets, and each second image set includes a plurality ofimages generated from a corresponding first image based on a neuralnetwork and the matching relationship between the first and secondimages is provided by storing the generated images in a database, basedon: (i) each second image set is matched to a respective one of thefirst images, and (ii) a plurality of images in each second image set isarranged based on a mutual positional relationship.
 9. The imageproviding method of claim 8, further comprising: in response to a changein the directivity during a process of providing the second images inascending order of corresponding locations, providing the second imagesin descending order of the corresponding locations, and in response to achange in the directivity during a process of providing the secondimages in descending order of corresponding locations, providing thesecond images in ascending order of the corresponding locations.
 10. Theimage providing method of claim 8, wherein the providing of the secondimages comprises sequentially providing the second images while thesecond user input is maintained, and, if the second user input isterminated, sequentially providing the first images based on thedirectivity, starting a first image of the first images corresponding toa second image provided at a point in time at which the second userinput is terminated.
 11. The image providing method of claim 8, whereinthe providing of the second images in order of the correspondinglocation based on the directivity comprises, if the second user input isadditionally received, sequentially providing the first images based onthe directivity, starting from a first image corresponding to a secondimage provided at a point in time at which the second user input isadditionally received.
 12. A non-transitory computer-readable recordmedium storing instructions that, when executed by a processor, causethe processor to perform the image providing method of claim
 8. 13. Theimage providing method of claim 8, wherein each second image set ismatched to the respective one of the first images, with reference to1^(st) image of the each second image set.
 14. The image providingmethod of claim 8, wherein the second image provided as a starting imagein response to receiving the second user input is 1^(st) image of asecond image set corresponding to the location of first image, which isprovided at a point in time at which the second user input is received.15. The image providing method of claim 8, wherein the plural secondimages are matched to the corresponding first image, based onidentification numbers of the plural second images and the correspondingfirst image.
 16. The image providing method of claim 8, wherein theneural network includes a neural network pre-trained by using, astraining data, a plurality of training image pairs each including afirst training image of the first slice thickness and a second trainingimage of the second slice thickness.